import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   int)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    tempfile.mkdtemp()
   _UpperCamelCase           =    5

   # Realm tok
   _UpperCamelCase           =    [
       "[UNK]",
       "[CLS]",
       "[SEP]",
       "[PAD]",
       "[MASK]",
       "test",
       "question",
       "this",
       "is",
       "the",
       "first",
       "second",
       "third",
       "fourth",
       "fifth",
       "record",
       "want",
       "##want",
       "##ed",
       "wa",
       "un",
       "runn",
       "##ing",
       ",",
       "low",
       "lowest",
   ]
   _UpperCamelCase           =    os.path.join(self.tmpdirname							, "realm_tokenizer")
   os.makedirs(lowercase_							, exist_ok=lowercase_)
   _UpperCamelCase           =    os.path.join(lowercase_							, VOCAB_FILES_NAMES["vocab_file"])
   with open(self.vocab_file							, "w"							, encoding="utf-8") as vocab_writer:
     vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

   _UpperCamelCase           =    os.path.join(self.tmpdirname							, "realm_block_records")
   os.makedirs(lowercase_							, exist_ok=lowercase_)







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	RealmTokenizer:


   """simple docstring"""







   return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname							, "realm_tokenizer"))







 def 		__UpperCAmelCase				(		self				:   int)						->	Tuple:


   """simple docstring"""







   shutil.rmtree(self.tmpdirname)







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    RealmConfig(num_block_records=self.num_block_records)
   return config







 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	int:


   """simple docstring"""







   _UpperCamelCase           =    Dataset.from_dict(
       {
           "id": ["0", "1"],
           "question": ["foo", "bar"],
           "answers": [["Foo", "Bar"], ["Bar"]],
       })
   return dataset







 def 		__UpperCAmelCase				(		self				:   str)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    np.array(
       [
           B"This is the first record",
           B"This is the second record",
           B"This is the third record",
           B"This is the fourth record",
           B"This is the fifth record",
           B"This is a longer longer longer record",
       ]							, dtype=lowercase_							, )
   return block_records







 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    RealmRetriever(
       block_records=self.get_dummy_block_records()							, tokenizer=self.get_tokenizer()							, )
   return retriever







 def 		__UpperCAmelCase				(		self				:   str)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    self.get_config()
   _UpperCamelCase           =    self.get_dummy_retriever()
   _UpperCamelCase           =    retriever.tokenizer

   _UpperCamelCase           =    np.array([0, 3]							, dtype="long")
   _UpperCamelCase           =    tokenizer(["Test question"]).input_ids
   _UpperCamelCase           =    tokenizer(
       ["the fourth"]							, add_special_tokens=lowercase_							, return_token_type_ids=lowercase_							, return_attention_mask=lowercase_							, ).input_ids
   _UpperCamelCase           =    config.reader_seq_len

   _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    retriever(
       lowercase_							, lowercase_							, answer_ids=lowercase_							, max_length=lowercase_							, return_tensors="np")

   self.assertEqual(len(lowercase_)							, 2)
   self.assertEqual(len(lowercase_)							, 2)
   self.assertEqual(len(lowercase_)							, 2)
   self.assertEqual(concat_inputs.input_ids.shape							, (2, 10))
   self.assertEqual(concat_inputs.attention_mask.shape							, (2, 10))
   self.assertEqual(concat_inputs.token_type_ids.shape							, (2, 10))
   self.assertEqual(concat_inputs.special_tokens_mask.shape							, (2, 10))
   self.assertEqual(
       tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0])							, ["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "first", "record", "[SEP]"]							, )
   self.assertEqual(
       tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1])							, ["[CLS]", "test", "question", "[SEP]", "this", "is", "the", "fourth", "record", "[SEP]"]							, )







 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.get_config()
   _UpperCamelCase           =    self.get_dummy_retriever()
   _UpperCamelCase           =    retriever.tokenizer

   _UpperCamelCase           =    np.array([0, 3, 5]							, dtype="long")
   _UpperCamelCase           =    tokenizer(["Test question"]).input_ids
   _UpperCamelCase           =    tokenizer(
       ["the fourth", "longer longer"]							, add_special_tokens=lowercase_							, return_token_type_ids=lowercase_							, return_attention_mask=lowercase_							, ).input_ids
   _UpperCamelCase           =    config.reader_seq_len

   _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    retriever(
       lowercase_							, lowercase_							, answer_ids=lowercase_							, max_length=lowercase_							, return_tensors="np")

   self.assertEqual([False, True, True]							, lowercase_)
   self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]							, lowercase_)
   self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]							, lowercase_)







 def 		__UpperCAmelCase				(		self				:   int)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    self.get_dummy_retriever()
   retriever.save_pretrained(os.path.join(self.tmpdirname							, "realm_block_records"))

   # Test local path
   _UpperCamelCase           =    retriever.from_pretrained(os.path.join(self.tmpdirname							, "realm_block_records"))
   self.assertEqual(retriever.block_records[0]							, B"This is the first record")

   # Test mocked remote path
   with patch("transformers.models.realm.retrieval_realm.hf_hub_download") as mock_hf_hub_download:
     _UpperCamelCase           =    os.path.join(
         os.path.join(self.tmpdirname							, "realm_block_records")							, _REALM_BLOCK_RECORDS_FILENAME)
     _UpperCamelCase           =    RealmRetriever.from_pretrained("google/realm-cc-news-pretrained-openqa")

   self.assertEqual(retriever.block_records[0]							, B"This is the first record")





import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   UNetaDModel							, lowercase_				:   UNetaDModel							, lowercase_				:   DDPMScheduler							, lowercase_				:   Any							, )						->	Any:


   """simple docstring"""







   super().__init__()
   _UpperCamelCase           =    value_function
   _UpperCamelCase           =    unet
   _UpperCamelCase           =    scheduler
   _UpperCamelCase           =    env
   _UpperCamelCase           =    env.get_dataset()
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].mean()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].std()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    env.observation_space.shape[0]
   _UpperCamelCase           =    env.action_space.shape[0]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Optional[int]							, lowercase_				:   Optional[Any])						->	Tuple:


   """simple docstring"""







   return (x_in - self.means[key]) / self.stds[key]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Tuple							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   return x_in * self.stds[key] + self.means[key]







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   if type(lowercase_) is dict:
     return {k: self.to_torch(lowercase_) for k, v in x_in.items()}
   elif torch.is_tensor(lowercase_):
     return x_in.to(self.unet.device)
   return torch.tensor(lowercase_							, device=self.unet.device)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   Any							, lowercase_				:   Union[str, Any]							, lowercase_				:   List[str])						->	Tuple:


   """simple docstring"""







   for key, val in cond.items():
     _UpperCamelCase           =    val.clone()
   return x_in







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   List[Any]							, lowercase_				:   int)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    x.shape[0]
   _UpperCamelCase           =    None
   for i in tqdm.tqdm(self.scheduler.timesteps):
     # create batch of timesteps to pass into model
     _UpperCamelCase           =    torch.full((batch_size,)							, lowercase_							, device=self.unet.device							, dtype=torch.long)
     for _ in range(lowercase_):
       with torch.enable_grad():
         x.requires_grad_()

         # permute to match dimension for pre-trained models
         _UpperCamelCase           =    self.value_function(x.permute(0							, 2							, 1)							, lowercase_).sample
         _UpperCamelCase           =    torch.autograd.grad([y.sum()]							, [x])[0]

         _UpperCamelCase           =    self.scheduler._get_variance(lowercase_)
         _UpperCamelCase           =    torch.exp(0.5 * posterior_variance)
         _UpperCamelCase           =    model_std * grad

       _UpperCamelCase           =    0
       _UpperCamelCase           =    x.detach()
       _UpperCamelCase           =    x + scale * grad
       _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)

     _UpperCamelCase           =    self.unet(x.permute(0							, 2							, 1)							, lowercase_).sample.permute(0							, 2							, 1)

     # TODO: verify deprecation of this kwarg
     _UpperCamelCase           =    self.scheduler.step(lowercase_							, lowercase_							, lowercase_							, predict_epsilon=lowercase_)["prev_sample"]

     # apply conditions to the trajectory (set the initial state)
     _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
     _UpperCamelCase           =    self.to_torch(lowercase_)
   return x, y







 def __call__(		self				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   int=64							, lowercase_				:   Any=32							, lowercase_				:   List[Any]=2							, lowercase_				:   str=0.1)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    self.normalize(lowercase_							, "observations")
   _UpperCamelCase           =    obs[None].repeat(lowercase_							, axis=0)

   _UpperCamelCase           =    {0: self.to_torch(lowercase_)}
   _UpperCamelCase           =    (batch_size, planning_horizon, self.state_dim + self.action_dim)

   # generate initial noise and apply our conditions (to make the trajectories start at current state)
   _UpperCamelCase           =    randn_tensor(lowercase_							, device=self.unet.device)
   _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
   _UpperCamelCase           =    self.to_torch(lowercase_)

   # run the diffusion process
   _UpperCamelCase      ,						_UpperCamelCase           =    self.run_diffusion(lowercase_							, lowercase_							, lowercase_							, lowercase_)

   # sort output trajectories by value
   _UpperCamelCase           =    y.argsort(0							, descending=lowercase_).squeeze()
   _UpperCamelCase           =    x[sorted_idx]
   _UpperCamelCase           =    sorted_values[:, :, : self.action_dim]
   _UpperCamelCase           =    actions.detach().cpu().numpy()
   _UpperCamelCase           =    self.de_normalize(lowercase_							, key="actions")

   # select the action with the highest value
   if y is not None:
     _UpperCamelCase           =    0
   else:
     # if we didn't run value guiding, select a random action
     _UpperCamelCase           =    np.random.randint(0							, lowercase_)

   _UpperCamelCase           =    denorm_actions[selected_index, 0]
   return denorm_actions




